Communicating Research Findings

PSCI 2270 - Week 12

Georgiy Syunyaev

Department of Political Science, Vanderbilt University

November 28, 2023

Plan for this week

Presentations next week
Communicating research findings
Q&A on projects

Presentations next week

Reminders

Complete Human Subjects Training provided by CITI
- You can receive the certificate for that through Vandy for free here (read guide here)
- You need to complete the basic module and upload it via brightspace (not through OSF)

Final presentations are next week
- Each of you will have 5-7 minutes for presentation with 3-5 minutes of feedback
- Sign up for your slot here by Thursday
- There will be pizza and drinks!

Q&A last 20 minutes of class on Thursday

Final presentations

5-7 minutes \(\approx\) 5-7 slides

Do not put too much text on the slides:
- \(6 \times 6\) rule: Unless absloutely unavoidable don’t put more than 6 words and 6 lines per slide
- Try not to read from slides

What to include: Motivation, Research Question, Hypotheses, Research Design (Context/Unit of analysis/Experiment or Observational), Independent/Dependent variables, Measurement and procedures

Communicating research findings

The issue with communication

There is a lot of information in the world
- There is even a lot of information in your individual projects… TOO MUCH

Your have two tasks
1. Understand what results you have: Descriptive, observational, experimental
2. Decide how to present your results: Simplify and tell a story with your data

In the end you will have a result from the data AND would want others to understand and believe in it

How we usually communicate

Two main approaches: Tables and Graphs

First can be more informative for advanced audience (who understand point estimates and standard errors)

Second can be more informative to general audience

Sometimes it is not enough to just look at the table! \(\Rightarrow\) Always plot your data!

Anscombe’s Quartet

F.J. Anscombe (1973): “…make both calculations and graphs. Both sorts of output should be studied; each will contribute to understanding.”

as_tibble(anscombe)

# A tibble: 11 × 8
      x1    x2    x3    x4    y1    y2    y3    y4
   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1    10    10    10     8  8.04  9.14  7.46  6.58
 2     8     8     8     8  6.95  8.14  6.77  5.76
 3    13    13    13     8  7.58  8.74 12.7   7.71
 4     9     9     9     8  8.81  8.77  7.11  8.84
 5    11    11    11     8  8.33  9.26  7.81  8.47
 6    14    14    14     8  9.96  8.1   8.84  7.04
 7     6     6     6     8  7.24  6.13  6.08  5.25
 8     4     4     4    19  4.26  3.1   5.39 12.5 
 9    12    12    12     8 10.8   9.13  8.15  5.56
10     7     7     7     8  4.82  7.26  6.42  7.91
11     5     5     5     8  5.68  4.74  5.73  6.89

Anscombe’s Quartet: Correlations 😎

There are four studies. Let’s look at the statistical relationship between \(X\) and \(Y\) for each of them

lm(y1 ~ x1, data = anscombe)


Call:
lm(formula = y1 ~ x1, data = anscombe)

Coefficients:
(Intercept)           x1  
     3.0001       0.5001

lm(y3 ~ x3, data = anscombe)


Call:
lm(formula = y3 ~ x3, data = anscombe)

Coefficients:
(Intercept)           x3  
     3.0025       0.4997

lm(y2 ~ x2, data = anscombe)


Call:
lm(formula = y2 ~ x2, data = anscombe)

Coefficients:
(Intercept)           x2  
      3.001        0.500

lm(y4 ~ x4, data = anscombe)


Call:
lm(formula = y4 ~ x4, data = anscombe)

Coefficients:
(Intercept)           x4  
     3.0017       0.4999

Note: We can also estimate linear correlation by either removing intercept y1 ~ -1 + x1 or by using cor() function

Anscombe’s Quartet: Long format 👌

Long format is useful for ggplot2: Stack \(X\)’s and \(Y\)’s for each study on top of each other
- In tidyverse we also call this long format tidy (which is where the tidy-verse is coming from)

as_tibble(anscombe_tidy)

# A tibble: 44 × 4
   study    id     x     y
   <chr> <int> <dbl> <dbl>
 1 1         1    10  8.04
 2 2         1    10  9.14
 3 3         1    10  7.46
 4 4         1     8  6.58
 5 1         2     8  6.95
 6 2         2     8  8.14
 7 3         2     8  6.77
 8 4         2     8  5.76
 9 1         3    13  7.58
10 2         3    13  8.74
# ℹ 34 more rows

lm(y ~ x, data = anscombe_tidy)


Call:
lm(formula = y ~ x, data = anscombe_tidy)

Coefficients:
(Intercept)            x  
     3.0013       0.4999

Anscombe’s Quartet: Pooled plot 🙆‍♂️

ggplot(data = anscombe_tidy, 
       mapping = aes(x = x, y = y)) +
  geom_smooth(method = lm, se = FALSE, color = "grey") +
  geom_point() + 
  coord_equal() +
  theme_bw()

Anscombe’s Quartet: Split plot 🤯

ggplot(data = anscombe_tidy, 
       mapping = aes(x = x, y = y, color = study)) +
  geom_smooth(method = lm, se = FALSE, color = "grey") +
  geom_point() +
  coord_equal() +
  facet_wrap(~ study) +
  theme_bw()

Anscombe 2.0 Cairo; Matejka & Fitzmaurice

Pew research

Which of the following describes the pattern on the plot?

A. In recent years, the rate of cavities has increased in many countries

B. In some countries, people brush their teeth more frequently than in other countries

C. The more sugar people eat, the more likely they are to get cavities

D. In recent years, the consumption of sugar has increased in many countries

Graphing types

Position along a common scale: Spatial location along a common baseline to represent data
- Example: Bar chart where the length of each bar represents a certain value, and each bar starts from the same baseline.

Position along non-aligned scales: Position is still used, but the baselines differ
- Example: Grouped bar chart where each group has a different baseline

Length, direction, angle: Comparisons based on length are fairly accurate. The direction is slightly less so, and the angle even less.
- Example: A bar chart (length) is usually more effective than a pie chart (angle)

Graphing types

Area: Area is less accurately perceived than the above
- Example: Circle size in a bubble chart

Volume and curvature: These are difficult for us to evaluate accurately
- Example: 3D charts that use volume to represent data

Shading, and colour saturation: These are the least accurately perceived
- Example: Heatmap

Hierarchy by Munzner (2014)

How would you test this?

Experimental evidence (!!)

Cleveland and McGill (1984): Foundational experiments

Heer and Bostock (2010): Using Amazon MTurk crowd-sourcing sample

Davis et al. (2022): Account for differences in respondent characteristics

Position > angle ? area ? volume \(\Rightarrow\) Position rules!

Common issues with graphs

Not enough information (e.g. improper or missing labels)

Hard to compare data (e.g. using 3D shapes)

Being intransparent (e.g. about scales)

Cutting data (e.g. use binary indicators instead of averages)

Too cluttered/Too much data

More examples here

Which plots we use

Workhorses
- Histograms
- Scatterplots
- Time trends
- Dot-whisker / Box plots

…Ponies

…Unicorns

Code

gapminder |> 
  ggplot2::ggplot(
    mapping = aes(x = gdpPercap)) +
  ggplot2::geom_histogram(bins = 30, fill = "lightgrey", color = "black") +
  ggplot2::labs(title = "Histogram of GDP Per Capita") +
  ggplot2::theme_minimal()

Code

gapminder |> 
  ggplot2::ggplot(
    mapping = aes(x = gdpPercap, y = lifeExp, color = continent)) +
  ggplot2::geom_point(alpha = 0.2) +
  ggplot2::stat_smooth(se = FALSE) +
  ggplot2::scale_x_log10() +
  ggplot2::labs(
    title = "Life Expectancy vs GDP Per Capita by Continent",
    x = "log(gdpPercap)") +
  ggplot2::theme_minimal()

Code

gapminder |> 
  dplyr::group_by(continent, year) |> 
  dplyr::summarise(lifeExp = mean(lifeExp, na.rm = TRUE)) |> 
  dplyr::mutate(type = "Continent average") |> 
  dplyr::bind_rows(gapminder) |> 
  dplyr::mutate(type = ifelse(is.na(type), "Country", type),
                country = ifelse(is.na(country), continent, country)) |> 
  ggplot2::ggplot(
    mapping = aes(x = year, y = lifeExp, 
                  group = country, color = type, alpha = type)) +
  ggplot2::geom_line(linewidth = 0.8, show.legend = FALSE) +
  ggplot2::facet_wrap(~continent, ncol = 2) +
  ggplot2::scale_alpha_manual(values = c(1, .1)) +
  ggplot2::labs(
    title = "Trends in Life Expectancy by Continent") + 
  ggplot2::theme_minimal()

Code

gapminder |> 
  ggplot2::ggplot(
    mapping = aes(x = continent, y = lifeExp)) +
  ggplot2::geom_boxplot(outlier.colour = "hotpink") +
  ggplot2::geom_jitter(position = position_jitter(width = 0.1, height = 0), 
                       alpha = 0.25) +
  ggplot2::labs(title = "Box Plot of Life Expectancy by Continent") +
  ggplot2::theme_minimal()

Resources

Data Visualization: Practical Guide and accompanying class
The R Graphics Cookbook
ggplot2 book
LOST-STATS
Stack-overflow
Internet search!

Next time

Practice with summary tables and workhorses in ggplot2
Q&A

References

Cleveland, William S., and Robert McGill. 1984. “Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods.” Journal of the American Statistical Association 79 (387): 531–54. https://doi.org/10.1080/01621459.1984.10478080.

Davis, Russell, Xiaoying Pu, Yiren Ding, Brian D. Hall, Karen Bonilla, Mi Feng, Matthew Kay, and Lane Harrison. 2022. “The Risks of Ranking: Revisiting Graphical Perception to Model Individual Differences in Visualization Performance.” IEEE Transactions on Visualization and Computer Graphics, 1–16. https://doi.org/10.1109/TVCG.2022.3226463.

Heer, Jeffrey, and Michael Bostock. 2010. “Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design.” In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 203–12.

Munzner, Tamara. 2014. Visualization Analysis and Design. CRC press.